Artificial intelligence based prediction models for individuals at risk of multiple diabetic complications: A systematic review of the literature

Abstract Aim The aim of this review is to examine the effectiveness of artificial intelligence in predicting multimorbid diabetes‐related complications. Background In diabetic patients, several complications are often present, which have a significant impact on the quality of life; therefore, it is crucial to predict the level of risk for diabetes and its complications. Evaluation International databases PubMed, CINAHL, MEDLINE and Scopus were searched using the terms artificial intelligence, diabetes mellitus and prediction of complications to identify studies on the effectiveness of artificial intelligence for predicting multimorbid diabetes‐related complications. The results were organized by outcomes to allow more efficient comparison. Key issues Based on the inclusion/exclusion criteria, 11 articles were included in the final analysis. The most frequently predicted complications were diabetic neuropathy (n = 7). Authors included from two to a maximum of 14 complications. The most commonly used prediction models were penalized regression, random forest and Naïve Bayes model neural network. Conclusion The use of artificial intelligence can predict the risks of diabetes complications with greater precision based on available multidimensional datasets and provides an important tool for nurses working in preventive health care. Implications for Nursing Management Using artificial intelligence contributes to a better quality of care, better autonomy of patients in diabetes management and reduction of complications, costs of medical care and mortality.


| INTRODUCTION
Due to the increased morbidity in recent years, it is estimated that 642 million people will be diagnosed with diabetes in 2040 (Zou et al., 2018). Therefore, it is particularly crucial to assess and predict the level of risk for diabetes and its complications (Erandathi et al., 2020). Given the high prevalence, many researchers and physicians have developed various detection techniques based on artificial intelligence (AI) to approach problems better and avoid human error (Sharma & Shah, 2021). AI research in health care is rapidly accelerating in various fields of medicine (Kelly et al., 2019). The introduction of new technological advances will allow greater autonomy and more personalized treatment of patients (Briganti & Le Moine, 2020). AI has great potential to enhance the care of common chronic conditions significantly (Tarumi et al., 2021).

| BACKGROUND
Managing chronic diseases is challenge for patients and health care providers (Tahri Sqalli & Al-Thani, 2020). Diabetes mellitus (DM) is a chronic disease that is becoming increasingly worrying due to its high morbidity and is considered life-threatening (Jian et al., 2021;Sharma & Shah, 2021). In DM2, it leads to an insulin resistance response, with a consequent decrease in insulin production. This occurs most often in people over 45 years of age (Goyal & Jialal, 2022). Eventually, it can affect any part of the body, causing serious complications (Jian et al., 2021), which can lead to multiple organ failures and have an impact on quality of life (Chaki et al., 2020;Ramesh et al., 2021). It is associated with a shorter life expectancy due to a higher risk of developing various diseases such as heart disease, stroke, blindness and amputation (Shah & Vella, 2014).
Diabetes-related complications are a concern because they are unrecognized in the early stages of their development. Over time, they can become immutable and devastating, so identifying a high-risk population and developing regular monitoring is crucial for prevention (Mosa et al., 2021;Ramesh et al., 2021). Complications range from acute complications, which are life-threatening conditions (hypoglycemia or ketoacidosis), to chronic, longer-lasting complications that can affect multiple organ systems (retinopathy, nephropathy, neuropathy, cardiovascular disease) (Nickerson & Dutta, 2012).
Patients with diabetes often have multimorbidity. The mentioned state describes several concomitant conditions with a significant impact on patient care and life quality (Chima et al., 2017). Predicting the development of disease complications is a demanding process due to the existence of unmeasured risk factors, unbalanced data, timevarying dynamics data and various interventions for the disease (Yousefi & Tucker, 2020).
Accurate prediction of complications could help with more targeted measures to prevent or slow their development (Ljubic et al., 2020). AI with predictive analytics has great potential to improve the care of common chronic conditions with high morbidity and mortality and an important role in maintaining a healthy lifestyle, taking medication and monitoring glycemic status (Behera, 2021;Tarumi et al., 2021). AI is the use of computers and advanced technologies to simulate intelligent behaviour and critical thinking (Malik et al., 2019). It is often used to support health care staff to carry out tasks ranging from administrative work to patient monitoring (Bohr & Memarzadeh, 2020).
The use of AI can provide significant improvements in all areas of health care from diagnosis to treatment (Bohr & Memarzadeh, 2020).
Studies show that AI methods are gradually being established as suitable for diabetes self-management (Contreras & Vehi, 2018). The challenge for the use of AI in these areas of health care is not whether the technologies are effective and useful but to ensure that they are introduced into everyday clinical practice (Davenport & Kalakota, 2019). The use of AI would relieve the burden on health care professionals and increase the quality of work performed by reducing the possibility of errors and increasing accuracy (Aung et al., 2021).
The purpose of this systematic literature review is to determine the effectiveness of predicting multimorbid diabetes-related complications with AI-based models and determine which methods provide the best results in terms of prediction performance.

| METHODS
The literature review was conducted according to the recommendations of Khan et al. (2003). In the first step, we addressed the review question: 'Which AI-based approaches are suitable for predicting multiple diabetes-related complications?'. By answering this question, we have identified which techniques can help reduce the risk of complications and help diabetes care. In a second step, we set inclusion and exclusion criteria and restrictions for the selection of the literature, which are presented below. We used the keywords 'artificial intelligence', 'diabetes mellitus type 2', 'prediction of complications' and other synonyms (Table 1) in PubMed, CINAHL, MEDLINE and Scopus databases to search the literature. It needs to be noted that we only focus on AI-based prediction models in this review, while other AIbased approaches (e.g. AI-based solutions in imaging or speech or text recognition or generation) are also present in the literature. The complete search strategy is shown in supporting information S1. The relevance and quality of the studies were assessed by two authors, who evaluated the eligibility of the articles based on predefined inclusion and exclusion criteria. In the last two steps, two authors extract the data from the articles and display them using an identification table.
The results were then interpreted and discussed by all authors.
To select relevant articles, we set the following inclusion criteria: (a) quantitative (e.g. case studies, randomized controlled trials, controlled trials), qualitative (e.g. interview, questionnaire, focus groups) studies and mixed-method studies; (b) relating to the research topic of predicting multimorbid complications in diabetes using AI and (c)  ). Articles using exclusively regression models were not included in this study, although some might argue that some elements of machine learning are also present in this type of typically used prediction models in health care. Usually, regression models were used as a baseline model for comparison with other AI-based techniques. In such cases, we extracted the prediction performance results of the regression-based prediction models as well. The results we aimed to extract from each study were areas under the curve (AUC) with corresponding confidence intervals (CIs) or standard deviations (SD). Since some studies did not report the AUC results, we also extracted accuracy, sensitivity and specificity where available. The exclusion criteria were as follows: (a) other types of surveys, such as cross-sectional, observational surveys, summaries, commentaries, protocols and cohort studies; (b) duplicates between databases; and (c) articles that do not include AI-based prediction models and do not predict multimorbid complications as a consequence of diabetes. We did not include any publication date limits when searching for the articles.
Two authors assessed the adequacy of the studies based on inclusion and exclusion criteria. In case of disagreement, we resolved this by discussion between the authors. The two authors also extracted key results and information from the articles and presented them in tabular form (Table 2 and supporting information S2).

| RESULTS
Based on the search string, we found a total of 251 results in the databases. Of these, 38 articles included the prediction of diabetes-related complications, but only 11 hits included studies where two or more complications were predicted ( Figure 1). Table 2 presents the characteristics of the included studies.
Supporting information S2 presents the prediction performance results of the identified models (AUC, CI, accuracy, sensitivity, specificity) for predicting diabetes-related complications. Based on our systematic review, we found that models most often predict the risk of developing diabetic neuropathy (n = 7) (Dagliati et al., 2018;Fan et al., 2021;Lagani et al., 2015;Liu et al., 2020;Ljubic et al., 2020;Ozdemir et al., 2020;Wang et al., 2021) and diabetic nephropathy (n = 6). Slightly less frequently, the authors predicted the development of diabetic retinopathy (n = 5) ( Figure 2). The most commonly predicted complication in the models, diabetic neuropathy, is also one of the most common complications of diabetes, leading to loss of sensory function in the patient (Feldman et al., 2019;Juster-Switlyk & Smith, 2016).
Patients diagnosed with type 2 diabetes are often at risk of developing multiple additional comorbidities (Cicek et al., 2021). Therefore, in our systematic review, we focused on models that allow the user to ranges from 0.685 up to 0.847. However, one needs to be aware that different studies employ different approaches to cross-validation and internal versus external evaluation which can influence the reliability of the reported results. In many cases, the description of the evaluation protocol is weak or even nonexistent. Aminian et al. (2020) considered 26 different baseline variables (demographics, medical history, laboratory data, medications) for prediction in their study. In addition to classical regression models, machine learning approaches were also used. They found that cardiovascular events are among the most frequent complications in patients with type 2 diabetes and obesity. Dagliati et al. (2018) (Fan et al., 2021).
Fifty-one clinical variables were used in the selected predictive models and included between five and fifteen risk factors depending on the specific outcome (Lagani et al., 2015). Lee et al. (2021) found that higher HbA1c and lipid measurements were associated with an increased risk of complications and various comorbidities. Ljubic et al. (2020) cite the RNN GRU model as the most accurate model, followed by the RNN LSTM model.

| DISCUSSION
When patients have additional chronic comorbid conditions, there is an almost exponential increase in the cost of care related to health care services, medicines and hospital admissions (McPhail, 2016).
Complications resulting from type 2 diabetes, such as nephropathy, neuropathy, blindness, cardiovascular disease and amputations reduce their quality of life and increase mortality. With advances in the care and treatment of type 2 diabetes and its complications, people with diabetes can live with their condition for longer (Deshpande et al., 2008;Liu et al., 2010). It is important to detect the development of complications early enough, as rapid action can prevent or delay the onset of chronic complications (Marshall & Flyvbjerg, 2006). AI plays an important role in predicting complications using basic clinical and biochemical patient data, but predicting the occurrence of different complications is a challenging task due to different risk factors, unbalanced data and rapid changes (Singla et al., 2019). Therefore, there is an increasing emphasis on the use of appropriate AI techniques to predict prognosis (Singla et al., 2019;Yousefi & Tucker, 2020). Consequently, accurate prediction helps to target nursing interventions better (Ljubic et al., 2020 Nurses, as the largest part of all workers involved in health systems, will benefit enormously from AI (Shang, 2021). The role of nurses is to be actively involved in decision-making regarding the implementation of AI in the health care system and to ensure that they ensure that these changes are implemented in accordance with the ethical principles and values of nursing (Buchanan et al., 2020).
With the introduction of technology, nurses' experience, knowledge and skills will be transformed into learning new ways of thinking and processing information (Robert, 2019). Our literature review also found that nurses are rarely involved in the interdisciplinary team that carries out the implementation. In most cases, it was individual research carried out by the researchers. It would be important to involve health care providers as they can influence the actual implementation of AI in clinical practice.
IT skills training should be offered to nursing students and those already working in a clinical setting (Risling, 2017). It is important that they understand the potential of AI and its impact on health care (Fritz & Dermody, 2019). It is also important that nurses are empowered by technological change and that they are not just passively involved in it (Ng et al., 2021).
In practice, there is still a lack of models or frameworks for implementing AI in everyday health care practices (Svedberg et al., 2022).
Yet, there are individual gaps in the literature on AI in nursing, with implications for clinical practice (Shang, 2021). The content of research in the field is very diverse, so it is important to develop guidelines on research reporting and technology implementation (von Gerich et al., 2021). This is also the problem we encountered in our literature review, and it is also the biggest limitation. The included

| IMPLICATIONS FOR NURSING MANAGEMENT
By using AI methods, we can help facilitate the control of diabetes and detect the presence of risk in patients for the development of multimorbid complications promptly. In this way, we contribute to a better quality of care, better autonomy of patients in the course of treatment of their disease and reduction of complications, costs of medical care and mortality. The use of AI methods also serves as a tool for nurses when working with patients, making it easier for them to predict disease progression and thus contributing better preventive care for patients.

CONFLICT OF INTEREST
The authors declare no conflict of interest.

ETHICS STATEMENT
Ethical approval was not required as the research does not involve any participants and only involves a review of the literature.

DATA AVAILABILITY STATEMENT
The data that support the findings of this study are available in the supporting information of this article.